Identifying Risk Factors for Secondary Infection Post-SARS-CoV-2 Infection
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE184401 | GEO | 2021/09/20
REPOSITORIES: GEO
Similar Datasets
Project description:Identifying Risk Factors for Secondary Infection Post-SARS-CoV-2 Infection
| PRJNA764515 | ENA
Project description:Emerging evidence has unveiled the secondary infection as one of the mortal causes of post-SARS-CoV-2 infection, but the factors related to secondary bacterial or fungi infection remains largely unexplored. We here systematically investigated the factors that might contribute to secondary infection. By clinical examination index analysis of patients, combined with the integrative analysis with RNA-seq analysis in the peripheral blood mononuclear cell isolated shortly from initial infection, this study showed that the antibiotic catabolic process and myeloid cell homeostasis were activated while the T-cell response were relatively repressed in those with the risk of secondary infection. Further monitoring analysis of immune cell and liver injury analysis showed that the risk of secondary infection was accompanied by severe lymphocytopenia at the intermediate and late stages and liver injury at the early stages of SARS-CoV-2. Moreover, the metagenomics analysis of bronchoalveolar lavage fluid and the microbial culture analysis, to some extent, showed that the severe pneumonia-related bacteria have already existed in the initial infection.
| S-EPMC8514874 | biostudies-literature
Project description:Dysregulated immune responses contribute to the excessive and uncontrolled inflammation observed in severe COVID-19. However, how immunity to SARS-CoV-2 is induced and regulated remains unclear. Here we uncover a role of the complement system in the induction of innate and adaptive immunity to SARS-CoV-2. Complement rapidly opsonizes SARS-CoV-2 particles via the lectin pathway. Complement-opsonized SARS-CoV-2 efficiently induces type-I interferon and pro-inflammatory cytokine responses via activation of dendritic cells, which are inhibited by antibodies against the complement receptors (CR) 3 and 4. Serum from COVID-19 patients, or monoclonal antibodies against SARS-CoV-2, attenuate innate and adaptive immunity induced by complement-opsonized SARS-CoV-2. Blocking of CD32, the FcγRII antibody receptor of dendritic cells, restores complement-induced immunity. These results suggest that opsonization of SARS-CoV-2 by complement is involved in the induction of innate and adaptive immunity to SARS-CoV-2 in the acute phase of infection. Subsequent antibody responses limit inflammation and restore immune homeostasis. These findings suggest that dysregulation of the complement system and FcγRII signaling may contribute to severe COVID-19.
| S-SCDT-10_1038-S44318-024-00061-0 | biostudies-other
Project description:Rapid dissemination of SARS-CoV-2 sequencing data to public repositories has enabled widespread study of viral genomes, but studies of longitudinal specimens from infected persons are relatively limited. Analysis of longitudinal specimens enables understanding of how host immune pressures drive viral evolution in vivo. Here we performed sequencing of 49 longitudinal SARS-CoV-2-positive samples from 20 patients in Washington State collected between March and September of 2020. Viral loads declined over time with an average increase in RT-PCR cycle threshold (Ct) of 0.87 per day. We found that there was negligible change in SARS-CoV-2 consensus sequences over time, but identified a number of nonsynonymous variants at low frequencies across the genome. We observed enrichment for a relatively small number of these variants, all of which are now seen in consensus genomes across the globe at low prevalence. In one patient, we saw rapid emergence of various low-level deletion variants at the N-terminal domain of the spike glycoprotein, some of which have previously been shown to be associated with reduced neutralization potency from sera. In a subset of samples that were sequenced using metagenomic methods, differential gene expression analysis showed a downregulation of cytoskeletal genes that was consistent with a loss of ciliated epithelium during infection and recovery. We also identified co-occurrence of bacterial species in samples from multiple hospitalized individuals. These results demonstrate that the intrahost genetic composition of SARS-CoV-2 is dynamic during the course of COVID-19, and highlight the need for continued surveillance and deep sequencing of minor variants.
2021-07-30 | GSE173310 | GEO
Project description:Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the contextual and spatial risk factors for PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified contextual and spatial risk factors from nearly 200 environmental characteristics for 23 PASC symptoms and conditions of eight organ systems. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each contextual and spatial factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) contextual and spatial characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), criteria air pollutants (e.g., sulfur dioxide), particulate matter (PM 2.5 ) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, respiratory, blood, circulatory, endocrine, and other organ systems. Specific contextual and spatial risk factors for each PASC condition and symptom were different across New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular contextual and spatial characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection.
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Project description:To explore the relationship between SARS-CoV-2 infection in different time before operation and postoperative main complications (mortality, main pulmonary and cardiovascular complications) 30 days after operation; To determine the best timing of surgery after SARS-CoV-2 infection.
| 12454 | ecrin-mdr-crc
Project description:HAE cultures were infected with SARS-CoV, SARS-dORF6 or SARS-BatSRBD and were directly compared to A/CA/04/2009 H1N1 influenza-infected cultures. Cell samples were collected at various hours post-infection for analysis. Time Points = 0, 12, 24, 36, 48, 60, 72, 84 and 96 hrs post-infection for SARS-CoV, SARS-dORF6 and SARS-BatSRBD. Time Points = 0, 6, 12, 18, 24, 36 and 48 hrs post-infection for H1N1. Done in triplicate or quadruplicate for RNA Triplicates/quadruplicates are defined as 3/4 different wells, plated at the same time and using the same cell stock for all replicates. Time matched mocks done in triplicate from same cell stock as rest of samples. Culture medium (the same as what the virus stock is in) will be used for the mock infections. Infection was done at an MOI of 2.
2013-07-31 | E-GEOD-47960 | biostudies-arrayexpress
Project description:HAE cultures were infected with SARS-CoV, SARS-ddORF6 or SARS-BatSRBD and were directly compared to A/CA/04/2009 H1N1 influenza-infected cultures. Cell samples were collected at various hours post-infection for analysis. Time Points = 0, 12, 24, 36, 48, 60, 72, 84 and 96 hrs post-infection for SARS-CoV. Time Points = 0, 24, 48, 60, 72, 84 and 96 hrs post-infection forSARS-ddORF6 and SARS-BatSRBD. Time Points = 0, 6, 12, 18, 24, 36 and 48 hrs post-infection for H1N1. Done in triplicate/quadruplicate for RNA Triplicates/quadruplicates are defined as 3/4 different wells, plated at the same time and using the same cell stock for all replicates. Time matched mocks done in triplicate from same cell stock as rest of samples. Culture medium (the same as what the virus stock is in) will be used for the mock infections. Infection was done at an MOI of 2.
2013-07-31 | E-GEOD-47961 | biostudies-arrayexpress
Project description:HAE cultures were infected with SARS-CoV, SARS-dORF6 or SARS-BatSRBD and were directly compared to A/CA/04/2009 H1N1 influenza-infected cultures. Cell samples were collected at various hours post-infection for analysis. Time Points = 0, 12, 24, 36, 48, 60, 72, 84 and 96 hrs post-infection for SARS-CoV, SARS-dORF6 and SARS-BatSRBD. Time Points = 0, 6, 12, 18, 24, 36 and 48 hrs post-infection for H1N1. Done in triplicate for RNA Triplicates are defined as 3 different wells, plated at the same time and using the same cell stock for all replicates. Time matched mocks done in triplicate from same cell stock as rest of samples. Culture medium (the same as what the virus stock is in) will be used for the mock infections. Infection was done at an MOI of 2 for SARS viruses and an MOI of 1 for H1N1.
2013-07-31 | E-GEOD-47962 | biostudies-arrayexpress
Project description:The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.
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